CryptoNumerics Announces CN-Protect for Data Science Python Library

CryptoNumerics , a Toronto-based enterprise software company, announced the launch of CN-Protect for Data Science which enables data scientists to implement state-of-the-art privacy protection, such as differential privacy, directly into their data science stack while maintaining analytical value.

According to a 2017 Kaggle study, two of the top 10 challenges that data scientists face at work are data inaccessibility and privacy regulations, such as GDPR, HIPAA, and CCPA. Additionally, common privacy protection techniques, such as Data Masking, often decimate the analytical value of the data. CN-Protect for Data Science solves these issues by allowing data scientists to seamlessly privacy-protect data sets that retain their analytical value and can subsequently be used for statistical analysis and machine learning.

“Private information that is contained in data is preventing data scientists from obtaining insights that can help meet business goals. They either cannot access the data at all or receive a low quality version which has had the private information removed.” Monica Holboke, Co-founder & CEO CryptoNumerics. “With CN-Protect for Data Science, data scientists can incorporate privacy protection in their workflow with ease and deliver more powerful models to their organization.”

CN-Protect for Data Science is a privacy-protection python library that works with Anaconda, Scikit and Jupyter Notebooks, smoothly integrating into the data scientist workflow. Data scientists will be able to:

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